Method for the computer-aided configuration of a data-driven model on the basis of training data

ABSTRACT

A method for the computer-assisted configuration of a data-driven model on the basis of training data is provided. The method is characterised in that the series of measurements are subjected to a suitable preprocessing process comprising a binning step, wherein measurement characteristics which existed during the measurement of the measurement values in question are taken into consideration. A suitable data-driven model such as a neural network is then learned on the basis of the pre-processed series of measurements. This learned data-driven model makes it possible to accurately forecast target vectors in accordance with associated series of measurements. The method can, for example, be used to analyse optical spectra. More particularly, it is possible to predict using the learned model whether the tissue sample for which an optical spectrum was detected represents diseased tissue.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority to PCT Application No.PCT/EP2018/065029, having a filing date of Jun. 7, 2018, which is basedoff of EP Application No. 17179817.6, having a filing date of Jul. 5,2017, the entire contents both of which are hereby incorporated byreference.

FIELD OF TECHNOLOGY

The following relates to a method for the computer-aided configurationof a data-driven model on the basis of training data. Embodiments of theinvention can be used in a multiplicity of technical fields ofapplication. By way of example, embodiments of the invention can be usedin the field of medical engineering to assist a doctor in classifyingtissue types.

BACKGROUND

By way of example, spectroscopic methods are used in many areas ofchemistry, food chemistry and biochemistry and also of biology andmedicine for determining or classifying substances. The methods involverecorded optical spectra being interpreted as characteristicfingerprints of the (bio)chemical composition of the examined sample andused for classification. In order to be able to use spectral data ofsingle samples for classification, prior knowledge of the typicalproperties of the classes to be distinguished is often necessary. Assuch, for example peaks in known spectral ranges are analyzed in orderto ascertain the water and oxygen content and hence to isolateconspicuous test samples. If classes whose biochemical and hencespectral properties are not or only partly known are supposed to bedistinguished, however, these methods cannot be used.

It is known practice from the prior art to learn data-driven models bymeans of measurement series, such as e.g. the spectra described above,as training data. For the optical spectra of the training data, theassociation thereof with applicable classes is known. The learneddata-driven model can then be used to ascertain the classes with whichan applicable optical spectrum is associated. Since training datarecords are frequently not available to a sufficient extent, thedata-driven models can often be learned only inadequately, however,which means that sufficient prediction accuracy is not ensured by thelearned data-driven models.

The document T. Yu et al., “Improving peak detection in high-resolutionLC/MS metabolomics data using preexisting knowledge and machine learningapproach”, BIOINFORMATICS, vol. 30, No. 20, Jul. 7, 2014, pages2941-2948, describes the detection of peaks in metabolomic data obtainedby means of liquid chromatography and mass spectrometry. The data aresubjected to machine learning in order to provide a model that candistinguish between actual peaks and noise. The method also involvesbinning of the metabolomic data being performed on the basis of localpoint density patterns.

The document S. Mahadevan, “DATA-BASED FAULT DETECTION AND DIAGNOSIS INBIOLOGICAL AND PROCESS SYSTEMS”, Doctoral thesis, Dec. 1, 2009,University of Alberta, Edmonton, Alberta, describes adaptive binning ofmetabolomic spectral NMR data. This binning is based on across-correlation analysis.

SUMMARY

An aspect relates to provide a method for the computer-aidedconfiguration of a data-driven model on the basis of training data thatresults in a data-driven model with high prediction accuracy even whenthe volume of training data is small.

The method according to embodiments of the invention is used for thecomputer-aided configuration of a data-driven model on the basis oftraining data. The training data comprise a plurality of data records. Arespective data record contains a measurement series, having a pluralityof first input variables having associated first measured values, and atarget vector, associated with the measurement series, comprising one ormore target variables having associated target values. The first inputvariables are successive on the basis of a prescribed order. Thisprescribed order therefore reproduces a sequence of the input variablesand of the measured values correlated therewith. By way of example, theprescribed order can be provided by physical parameters, such asenergies or wavelengths, wherein the sequence corresponds to thedirection toward larger or smaller values of the physical parameters.Similarly, the prescribed order may be represented by time, which issynonymous with a respective measurement series representing a timeseries of successive measured values.

In step a) of the method according to embodiments of the invention, themeasurement series of the training data are each subjected to apreprocessing, as a result of which modified training data comprisingmodified data records are obtained. A respective modified data recordcomprises a preprocessed measurement series and the same target vectoras the measurement series without preprocessing. A preprocessedmeasurement series comprises a plurality of second input variableshaving associated second measured values ascertained on the basis of thefirst measured values.

The preprocessing according to embodiments of the invention involves abinning step being performed in which first measured values of first andin particular adjacent first input variables of the respectivemeasurement series are combined on the basis of one or more measurementcharacteristics to produce measured value sections having associatedsection values. The measured value characteristic(s) were available forthe respective measurements from which the respective first measuredvalues were obtained. In an exemplary variant, the binning step isperformed directly in the space of the first measured values.Nevertheless, there is also the possibility of the binning step beingapplied to values that come from a conversion from the first measuredvalues.

In accordance with the binning step of the method according toembodiments of the invention, the number of first (adjacent) inputvariables combined to produce measured value sections is stipulated.This number can vary across the measurement series. Furthermore, it mayalso be possible for the offset of the individual measured valuesections to be stipulated in suitable fashion as part of the binning.

In an exemplary variant, the above-defined plurality of second inputvariables corresponds to the measured value sections, and the secondmeasured values are the section values. Nevertheless, after the binningstep, a further preprocessing step can be performed as part of thepreprocessing. In this case, the second input variables and secondmeasured values may be different variables than the measured valuesections and section values.

The binning step can further also involve only portions of the firstinput variables being combined to produce measured value sections. Inother words, after the binning step is performed, there may beoccasional instances of first input variables also not being correlatedwith further first input variables. These occasional input variables areprocessed in the method according to embodiments of the invention asmeasured value sections with the corresponding first measured value assection value.

In a step b) of the method according to embodiments of the invention,the data-driven model is learned in computer-aided fashion on the basisof the modified training data, wherein the learned data-driven modelallows the determination of target vectors on the basis of preprocessedmeasurement series.

The method according to embodiments of the invention is distinguished inthat information concerning the capture of the measurement data is takeninto consideration in suitable fashion in order to achievephysical-circumstances-based adaptation of the measurement series usedas training data and thereby to increase the information content of themeasurement data. This allows a good level of quality for the learneddata-driven model to be ensured even when the number of training datarecords is limited.

In a particularly exemplary embodiment, the measurementcharacteristic(s) comprise(s) the noise in the respective measurements,wherein the binning step combines first measured values of first and inparticular adjacent first input variables of the respective measurementseries such that the average signal-to-noise ratio is maximized over themeasured value sections, wherein, as a secondary condition of themaximization, allowances are made for a prescribed characteristic signalshape, e.g. in the form of peak widths in signal profiles, beingpreserved. Suitable optimization methods for performing this variant ofembodiments of the invention are known per se and are therefore notdescribed in detail. The embodiment just described ensures a high levelof quality for the training data used for the learning of thedata-driven model. The optimized data quality and the associatedimproved information content allow a good classification model to beobtained even when learning with few test or learning data.

Besides the binning step described above, the preprocessing in step a)of the method according to embodiments of the invention can also containone or more further preprocessing steps still. In an exemplaryembodiment, the preprocessing further comprises a rescaling and/ornormalization of the first measured values or of values derivedtherefrom.

In a further, particularly exemplary embodiment, the section values ofthe measured value sections defined above are determined by means of anaveraging over the first measured values of the respective measuredvalue sections. In other words, the section values are a mean value ofthese first measured values. Depending on the configuration, it ispossible for any desired mean value to be determined. In particular, theaveraging can be a weighted or unweighted averaging. In an exemplaryvariant, the mean value determined is the arithmetic mean value.Similarly, however, the median can also be determined as the mean valueor averaged over a Gaussian profile.

In a further variant of the method according to embodiments of theinvention, the preprocessing in step a) for each measurement seriesinvolves the first derivative and/or the second derivative beingdetermined according to an order parameter characterizing the prescribedorder, wherein the first derivative and/or the second derivative areincluded as second measured values of second input variables in thepreprocessed measurement series. In this manner, the information contentin the measurement series is increased further, which allows thelearning of the data-driven model to be improved. Depending on theconfiguration, the order parameter may be stipulated differently. In thecase of an optical spectrum, the order parameter may be e.g. thewavelength or a variable derived therefrom, such as e.g. the energy.Further, the order parameter can be time if the measurement series is atime series.

In a particularly exemplary embodiment, the data-driven model used inthe method according to embodiments of the invention is a neural networkcomprising an input layer, one or more hidden layers and an outputlayer, wherein the input layer obtains preprocessed measurement seriesas inputs and, on the basis of these inputs, generates outputs in theform of corresponding target vectors.

In an exemplary variant, the neural network used is what is known as aCNN (convolutional neural network) network. Such networks are known perse and contain as hidden layer at least one convolutional layer thatuses a linear transformation to generate what is known as a feature map.There is also provision for at least one pooling layer, which reducesthe dimension of the features of the feature map. In an exemplaryconfiguration, the CNN network is a deep CNN network, which is alsoreferred to as a DCNN (deep convolutional neural network) network andcomprises a multiplicity of hidden layers.

Embodiments of the invention are not restricted to the use of neuralnetworks as data-driven models, however. Rather, the data-driven modelcan also comprise a support vector machine and/or a cluster method (e.g.k-means clustering) and/or a decision tree and/or a PLS (partial leastsquares) regression. The PLS regression used in a specific variant is aPLSDA (partial least squares discriminant analysis) regression. Allcited data-driven models are known from the prior art and are thereforenot explained in detail.

In a further exemplary variant of the method according to embodiments ofthe invention, one or more target variables of the respective targetvector each describe an association or nonassociation with a class. Inthis case, the data-driven model is used for classifying applicablemeasurement series. Nevertheless, a target variable can also be avariable having a multiplicity of continuous or discrete values.

In a particularly exemplary embodiment of the invention, the measurementseries of the training data are optical spectra for respective objects,wherein an optical spectrum for a respective object comprises firstmeasured values represented by an absorption or a transmission ofelectromagnetic radiation for the respective object on the basis ofspectral values that are dependent on the wavelength of theelectromagnetic radiation. The target vector specifies one or morefeatures of the respective object as target variables. In oneconfiguration, the spectral values directly represent the wavelength ofthe electromagnetic radiation. Nevertheless, the spectral values cane.g. also represent the energy of the radiation.

The embodiment just described can be used to analyze optical spectra ofany desired materials in order to ascertain features of the materials inthat way. In an exemplary variant, a respective object is a biologicaltissue sample of the human or animal body. The target vector in thatcase comprises a feature that specifies the biological tissue sample aspathological or nonpathological, e.g. as tumorous or nontumorous.Similarly, it is possible for a respective object to be an organicsample. The target vector comprises one or more features specifying thetype of the organic sample and/or the aging state thereof.

In a particularly exemplary variant of embodiments of the invention, inwhich the measurement series are optical spectra, the measurementcharacteristic(s) taken into consideration in the binning steprepresent(s) the spectral resolution of the optical spectra on the basisof the spectral values. In that case, the number of first measuredvalues combined as part of the binning step decreases monotonously asthe spectral resolution increases. If the spectral values arewavelengths, the spectral resolution is normally greater toward higherwavelengths, with the number of combined first measured valuesdecreasing toward higher wavelengths in this case.

An embodiment in which the measurement series are optical spectra hasbeen described above. Nevertheless, embodiments of the invention are notrestricted thereto and the measurement series can also be otherorganized measurement data. As already mentioned above, the measurementseries can also be time series comprising first measured values obtainedfrom measurements at different times.

Besides the above-described method for configuring a data-driven model,embodiments of the invention also relate to a method for thecomputer-aided determination of a target vector on the basis of ameasurement series having a plurality of first input variables andassociated first measured values, wherein the target vector comprisesone or more target variables having target values to be determined andwherein the first input variables are successive on the basis of aprescribed order.

This method involves a learned data-driven model being provided that islearned using the method according to embodiments of the invention orone or more exemplary embodiments of the method according to embodimentsof the invention. In this case, the measurement series underconsideration is subjected to the same preprocessing as a respectivemeasurement series of the training data in step a) of the method used tolearn the data-driven model, as a result of which a preprocessedmeasurement series is obtained. The learned data-driven model is thenused to determine the target vector on the basis of the preprocessedmeasurement series. The method therefore allows the prediction orascertainment of target vectors for measurement series for which thetarget vectors are not known.

Besides the above-described methods according to embodiments of theinvention, embodiments of the invention further relate to a computerprogram product (non-transitory computer readable storage medium havinginstructions, which when executed by a processor, perform actions)having a program code, stored on a machine-readable medium, forperforming the method according to embodiments of the invention for thecomputer-aided configuration of a data-driven model or the methodaccording to embodiments of the invention for the computer-aideddetermination of a target vector or for performing one or more exemplaryvariants of these methods.

Furthermore, embodiments of the invention relate to a computer programhaving a program code for performing the method according embodiments ofto the invention for the computer-aided configuration of a data-drivenmodel or the method according to embodiments of the invention for thecomputer-aided determination of a target vector or one or more exemplaryvariants of these methods.

BRIEF DESCRIPTION

Some of the embodiments will be described in detail, with reference tothe following figures, wherein like designations denote like members,wherein:

FIG. 1 shows a schematic depiction of a neural network used as adata-driven model in a variant of the invention; and

FIG. 2 shows a flowchart clearly showing the steps of an embodiment ofthe method according to the invention.

DETAILED DESCRIPTION

A variant of embodiments of the invention is described below on thebasis of the computer-aided configuration of a data-driven model in theform of a neural network. A neural network NN of this kind is depictedschematically in FIG. 1 . This network is a DCNN network, which hasalready been mentioned above.

The network NN comprises an input layer IL, multiple hidden layers HL1,HL2, . . . , HLn and an output layer OL. The neural network is used toascertain or forecast a target vector comprising one or more features onthe basis of a measurement series of measured values. This target vectoris output via the output layer OL. In the embodiment described in thepresent case, measurement series relating to optical spectra of tissuesamples are considered. This involves NIR (near infrared) spectra, withfor example absorption spectra being considered. These spectra representthe absorption or the absorption coefficient of the tissue sample forlight having different wavelengths. Following appropriate learning ofthe neural network NN, it is then possible to predict whether or not thetissue sample having the associated spectrum is part of a tumor.

In contrast to conventional methods, the measurement series are notsupplied directly to the input layer IL of the neural network NN asinput variables, but rather are subjected to a preprocessing beforehandin order to make suitable allowances for prior knowledge concerning thecapture of the individual measured values. This preprocessing is alsoused when learning the neural network on the basis of applicabletraining data. The training data are data records comprising amultiplicity of measurement series in the form of the aforementionedoptical spectra, it being known in advance whether or not the opticalspectrum is associated with a tumorous tissue. In other words, a datarecord of the training data includes not only the optical spectrum butalso a target vector that, in the example under consideration in thepresent case, is a classification of the tissue as tumorous ornon-tumorous.

Nevertheless, the method according to embodiments of the invention isalso usable for other types of target vectors, which can also comprisemultiple features, a respective feature also being able to berepresented by the applicable value of a variable. In this case, e.g.value ranges of a variable can characterize different states ofmaterials for which the optical spectra have been recorded. For examplethey can be used to categorize the aging state of materials (such ase.g. oils).

FIG. 2 shows the learning of the neural network from FIG. 1 . Trainingdata TD comprising a multiplicity of data records DS are used for thelearning. Each data record contains an experimentally determinedmeasurement series MR comprising a multiplicity of input variables EGarranged in succession on the basis of a prescribed order. Each inputvariable is correlated with a measured value MW, the input variables ofdifferent data records DS being assigned different measured values MW.As mentioned, measurement series in the form of optical spectra oftissue samples are considered in the embodiment described in the presentcase, an applicable spectrum being indicated in the graph DI of FIG. 2 .

The abscissa of the graph DI indicates the wavelength λ of the lightshone onto the applicable tissue sample. The ordinate reproduces themeasured values MW, which correspond to absorption coefficients for thedifferent wavelengths in the example considered in the present case.This results in the measurement series MR in the form of the depictedcurve. In the example under consideration, the order of the inputvariables is achieved by means of the different wavelengths λ. In otherwords, the wavelength λ is an order parameter to describe the order ofthe input variables.

Each spectrum of a data record DS of the training data TD is correlatedwith a target vector ZV, which, in the embodiment described in thepresent case, comprises a single target variable ZG to which anapplicable target value ZW is assigned. This target value is theinformation concerning whether or not the tissue sample of theapplicable data record DS is tumorous.

The training data just described are taken as a basis for performing thelearning of the neural network NN. The training data are not useddirectly for this learning, however, but rather are subjected to apreprocessing, which is indicated by step S1 of FIG. 2 . Thepreprocessing can comprise various substeps. What is essential toembodiments of the invention, however, is that it contains a binningstep BS. This binning step involves measured values determined foradjacent wavelengths being combined by taking into considerationmeasurement characteristics MC relating to the measurement of therespective measured values. The combined measured values are measuredvalue sections that each have an associated section value, which is themean value of the combined measured values in the example underconsideration in the present case.

In the embodiment described in the present case, one measurementcharacteristic taken into consideration is the noise in the respectivemeasurement. In this case, the number of combined measured values ischosen such that the mean signal-to-noise ratio is maximized over thecombined measured value sections and at the same time the signal shapestill remains clearly identifiable over the resulting average.Appropriate methods of solving such an optimization problem aresufficiently well known to a person skilled in the art and are thereforenot explained in detail. In the example under consideration, it isfurthermore allowable for the number of measured values combined not toremain constant, but rather to be able to vary on the basis of thewavelength λ.

As an alternative or in addition to the cited maximization of thesignal-to-noise ratio, the method of FIG. 2 can also involve taking intoconsideration the measured value characteristic of the spectralresolution of the measured values within the respective optical spectra.In this case, a benefit is derived from the inside that the resolutionincreases toward greater wavelengths, since there are lower energiesthere and e.g. the characteristic eigenmodes of the chemical groups ofthe material differ from one another in fingerprint style in the middleinfrared. More distinct peaks in the spectrum are thus obtained atgreater wavelengths. This measured value characteristic can nowinfluence the preprocessing such that the number of measured valuescombined to produce measured value sections decreases toward higherwavelengths λ on account of the higher spectral resolution.

The preprocessing in accordance with step S1 of FIG. 2 can also comprisefurther steps in addition to the binning step BS. In particular, themeasured values can be rescaled or normalized. For example, the measuredvalues or the measured value sections resulting therefrom can be plottednot along a wavelength scale but rather along an energy scale, which isa nonlinear rescaling.

Furthermore, the preprocessing can also involve yet further measuredvalues being added that come from the original measured values by virtueof mathematical calculations. In particular, e.g. the gradient (firstderivative) or the curvature (second derivative) of the curve of therespective optical spectra can be considered as a further variable.

The result obtained from the preprocessing step S1 is finally modifiedtraining data TD′ consisting of modified data records DS′. A respectivemodified data record DS' contains a measurement series MR′, obtained inaccordance with the above preprocessing, that is characterized by newinput variables EG′ in the form of measured value sections MW′ withassociated section values. Independently of this, the target vector ZVhaving the target variable ZG and the target value ZW for the modifieddata record DS' remains unchanged, i.e. the target vector is the sametarget vector as in the case of the unmodified data record DS.

On the basis of the training data TD′, the neural network NN of FIG. 1is finally learned. As input data, this network obtains the preprocessedmeasurement series MR′ with the modified input variables EG′. Thepreprocessing has involved ensuring that the number of modified inputvariables EG′ is the same for all data records DS′. The neural networkis learned using methods known per se, and said learning is thereforenot described in more detail. The result obtained is the learned neuralnetwork NN, which is subsequently used in the forecast step PR depictedin FIG. 2 to correctly determine the applicable target vector ZVN for anew measurement series MRN with an unknown target vector. The step PRinvolves the same preprocessing being performed as was applied to thetraining data when learning the neural network. In other words, the samebinning is applied to a respective measurement series MRN to producepreprocessed measurement series MRN′. If need be, further preprocessingsteps still are also performed, provided that they were also implementedfor the training data. The result obtained for the step PR is finally acalculated target vector ZVN that, in the present case, indicateswhether the optical spectrum is associated with a tissue sample that istumorous. In a modified embodiment, it is also possible for a suitablemultidimensional target vector to be used that further specifies whattype of tissue, such as e.g. muscle, blood vessel, tendons, nerves, fat,etc., the tissue sample is associated with.

The embodiment of the invention that has been described above has aseries of advantages. In particular, a method for learning a data-drivenmodel is provided in which the measured data under consideration arepreprocessed in suitable fashion by taking into considerationmeasurement characteristics, as a result of which it is possible forlearning adapted to the physical circumstances of the measurement to beachieved. Specifically, the data quality before the actual learningprocess can be improved and this allows the prediction quality of thelearned data-driven models to be increased. At the same time, thequality of the learning can be improved even when the number of trainingdata records is small. This is very useful and advantageous particularlyin the case of medical issues, since access to large volumes of data intraining and validation phases is very complex or almost impossible.

The method according to embodiments of the invention can be used in amultiplicity of areas of application. As described above, e.g. opticalspectra can be analyzed. Nevertheless, there is also the possibility ofmeasurement series in the form of time series with successive measuredvalues being processed.

Although the present invention has been disclosed in the form ofpreferred embodiments and variations thereon, it will be understood thatnumerous additional modifications and variations could be made theretowithout departing from the scope of the invention.

For the sake of clarity, it is to be understood that the use of “a” or“an” throughout this application does not exclude a plurality, and“comprising” does not exclude other steps or elements.

The invention claimed is:
 1. A method for a computer-aided configurationof a data-driven model on a basis of training data, which comprise aplurality of data records, wherein a respective data record contains ameasurement series, having a plurality of first input variables havingassociated first measured values, and a target vector, associated withthe measurement series, comprising one or more target variables havingassociated target values, wherein the first input variables aresuccessive on a basis of a prescribed order, wherein: a) the measurementseries of the training data are each subjected to a preprocessing, as aresult of which modified training data comprising modified data recordsare obtained, wherein a respective modified data record comprises apreprocessed measurement series and the same target vector as themeasurement series without preprocessing, wherein the preprocessedmeasurement series has a plurality of second input variables havingassociated second measured values, which are ascertained on the basis ofthe first measured values, wherein the preprocessing contains a binningstep in which first measured values of first input variables of therespective measurement series are combined on the basis of one or moremeasurement characteristics to produce measured value sections havingassociated section values, wherein the measurement characteristics wereavailable for the respective measurements from which the respectivefirst measured values were obtained; b) the data-driven model is learnedin computer-aided fashion on the basis of the modified training data,wherein the learned data-driven model allows the determination of targetvectors on the basis of preprocessed measurement series.
 2. The methodas claimed in claim 1, wherein the measurement characteristics comprisethe noise in the respective measurements, wherein the binning stepcombines first measured values of first input variables of therespective measurement series such that the average signal-to-noiseratio is maximized over the measured value sections.
 3. The method asclaimed in claim 1, wherein the preprocessing in step a) comprises arescaling and/or normalization of the first measured values or of valuesderived therefrom.
 4. The method as claimed in claim 1, wherein thesection values of the measured value sections are determined by means ofan unweighted or weighted averaging over the first measured values ofthe respective measured value sections.
 5. The method as claimed inclaim 1, wherein the preprocessing in step a) for each measurementseries involves the first derivative and/or the second derivative beingdetermined according to an order parameter characterizing the prescribedorder, wherein the first derivative and/or the second derivative areincluded as second measured values of second input variables in thepreprocessed measurement series.
 6. The method as claimed in claim 1,wherein the data-driven model comprises a neural network comprising aninput layer, one or more hidden layers and an output layer, wherein theinput layer obtains preprocessed measurement series as inputs and, onthe basis of these inputs, generates outputs in the form ofcorresponding target vectors.
 7. The method as claimed in claim 6,wherein the neural network comprises a convolutional neural network(CNN) network.
 8. The method as claimed in claim 1, wherein thedata-driven model comprises a support vector machine and/or a clustermethod and/or a decision tree and/or a partial least squares (PLS)regression and/or a partial least squares discriminant analysis (PLSDA)regression.
 9. The method as claimed in claim 1, wherein one or moretarget variables of the respective target vector each describe anassociation or nonassociation with a class.
 10. The method as claimed inclaim 1, wherein the measurement series of the training data are opticalspectra for respective objects, wherein an optical spectrum for arespective object comprises first measured values represented by anabsorption or a transmission of electromagnetic radiation for therespective object on the basis of spectral values that are dependent onthe wavelength of the electromagnetic radiation, wherein the targetvector specifies one or more features of the respective object as targetvariables.
 11. The method as claimed in claim 10, wherein a respectiveobject is a biological tissue sample, wherein the target vectorcomprises a feature that specifies the biological tissue sample aspathological or nonpathological, or wherein a respective object is anorganic sample, wherein the target vector comprises one or more featuresspecifying the type of the organic sample and/or the aging statethereof.
 12. The method as claimed in claim 10, wherein the measurementcharacteristic comprise the spectral resolution of the optical spectraon the basis of the spectral values, wherein the number of firstmeasured values combined as part of the binning step decreasesmonotonously as the spectral resolution increases.
 13. The method asclaimed in claim 1, wherein the measurement series of the training dataare time series comprising first measured values obtained frommeasurements at different times.
 14. A method for the computer-aideddetermination of a target vector on a basis of a measurement serieshaving a plurality of first input variables having associated firstmeasured values, wherein the target vector comprises one or more targetvariables having target values to be determined and wherein the firstinput variables are successive on a basis of a prescribed order,wherein: a learned data-driven model is provided that is learned using amethod as claimed in claim 1; the measurement series is subjected to thesame preprocessing as a respective measurement series of the trainingdata in step a) of the method used to learn the data-driven model, as aresult of which a preprocessed measurement series is obtained; thelearned data-driven model is used to determine the target vector on thebasis of the preprocessed measurement series.
 15. A computer programproduct, comprising a computer readable hardware storage device havingcomputer readable program code stored therein, said program codeexecutable by a processor of a computer system to implement the methodas claimed in claim 1 when the program code is executed.